Single Set Of Parameters Research Articles

Triaxial mechanical characterization of ultrasoft 3D support bath-based bioprinted tubular GelMA constructs

Support bath-based extrusion bioprinting is an emerging additive manufacturing paradigm that allows for the creation of geometrically complex three-dimensional tissue-mimicking constructs. Although this approach enables arbitrary geometries to be printed, preserving shape and mechanical integrity after the construct is extracted from the support bath is one of the main challenges today. In the present study, we systematically tested the effect of critical printing parameters on construct integrity and stiffness. Specifically, we varied the concentration and temperature of the bioink and support bath material, hydrogel crosslinking time, and cell density of the bioink. While previous studies on hydrogel materials quantify construct properties based on a single loading mode-such as uniaxial testing or rheological experiments, for example—we used a multiaxial mechanical testing protocol to assess the printed construct’s response in tension, compression, and shear. For each sample, we determined a single set of material parameters by simultaneously fitting all three modes in our inverse finite element framework. In support of future modeling work, we analyzed three different constitutive models with respect to their ability to match the construct’s mechanical response. We observed that GelMA concentration, temperature, and cell density have a statistically significant effect on the construct stiffness; support bath temperature and UV crosslinking time have a weak effect on construct stiffness; and support bath concentration appears to have no direct effect on the measured construct properties. Our construct stiffness were found to vary between 0.07 and 2.2 kPa depending on printing conditions, and showed noticeable tension–compression asymmetry as well as pronounced nonlinear behavior when loaded under shear.

Thermodynamic re-optimization of the CaO–SiO2 system integrated with experimental phase equilibria studies

CaO–SiO2 is the key system in ferrous and non-ferrous metallurgy, ceramic manufacture, cement production, and other applications. A number of discrepancies and gaps in knowledge has been identified and resolved since the last thermodynamic optimization of this system. This study reports new experimental phase equilibria data measured using recent advances in equilibration, sample quenching, and Electron Probe X-ray Microanalysis (EPMA) technique. An updated self-consistent set of thermodynamic model parameters is provided. Most importantly, these updates are integrated with a complete re-assessment of the 20-component Cu–Pb–Zn–Fe–Ca–Si–O–S–Al–Mg–Cr–Na–As–Sn–Sb–Bi–Ag–Au–Ni–Co system directly relevant to applications in the ferrous and non-ferrous metallurgical industries. To obtain a large single set of model parameters, significant advances were made in the computational approach, with a focus on simultaneous parallel optimization of binary and ternary model parameters using an experimental dataset combining binary, ternary, and multicomponent information. The Modified Quasichemical Formalism is used for the liquid slag phase. Thermodynamic properties of solid phases and liquid endmembers are completely revised following the recommendations for the third generation of CALPHAD databases. Other notable changes include the revision of the melting temperature of CaO, justified by extrapolations from the multicomponent systems, as well as a physically realistic heat capacity function for metastable SiO2 below the glass transition temperature. These updated properties expand the areas of application of the database to amorphous and partially crystallized slags at ambient temperatures, such as thermodynamics and kinetics of leaching of toxic elements in an aqueous environment. The fundamental limitations of the existing Modified Quasichemical Formalism are also discussed, along with strategies for addressing them.

High-dimensional detection of Landscape Dynamics: a Landsat time series-based algorithm for forest disturbance mapping and beyond

ABSTRACT Time series analysis of medium-resolution multispectral satellite imagery is critical to investigate forest disturbance dynamics at the landscape scale. In particular, the spatial, temporal, and radiometric consistency of Landsat time series data provides unprecedented insight into past disturbances that occurred over the last four decades. Several Landsat time series-based algorithms have been developed to automate the detection of forest disturbances. However, automated detection of non-stand-replacing disturbances based on Landsat time series remains a challenging task due to the difficulty of effectively separating them from spectral noise. Here, we present the High-dimensional detection of Landscape Dynamics (HILANDYN) algorithm, which exploits spatial and spectral information provided by Landsat time series to detect forest disturbance dynamics retrospectively. A novel and unsupervised procedure for changepoint detection in high-dimensional time series allows HILANDYN to perform the temporal segmentation of inter-annual time series into linear trends. The algorithm embeds a noise filter to remove spurious changepoints caused by residual spectral noise in the time series. We tested HILANDYN to detect disturbances that occurred in the forests of the European Alps over a period of 39 years, i.e. between 1984 and 2022, and evaluated its accuracy using a validation dataset of 3000 plots randomly located inside and outside the disturbed patches. We compared HILANDYN with the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST), which is a well-established and high-performing time series-based algorithm for changepoint detection. The quantitative results highlighted that the number of bands, i.e. original Landsat bands and spectral indices, included in the high-dimensional time series and the threshold controlling the significance of changepoints strongly influenced the user’s accuracy (UA). Conversely, changes in the combinations of bands primarily affected the producer’s accuracy (PA). HILANDYN achieved an F1 score of 0.801, which increased to 0.833 when we activated the noise filter, allowing the algorithm to balance UA (83.1%) and PA (83.5%). The qualitative results showed that disturbed forest patches detected by HILANDYN were characterized by a high spatio-temporal consistency, regardless of the disturbance severity. Furthermore, our algorithm was able to detect forest patches associated with secondary disturbances, such as salvage logging, that occur in close succession with respect to the primary event. The comparison with BEAST evidenced a similar sensitivity of the algorithms to non-stand-replacing events, as both achieved comparable PA. However, BEAST struggled to balance UA and PA when using a single parameter set, achieving a maximum F1 score of 0.717. Moreover, the computational efficiency of BEAST in processing high-dimensional time series was very limited due to its univariate nature based on the Bayesian approach. The adaptability of HILANDYN to detect a wide range of disturbance severities using a single parameter set and its computational efficiency in handling high-dimensional time series promotes its scalability to large study areas characterized by heterogeneous ecological conditions.

Extending a Multiphysics Li-Ion Battery Model from Normal Operation to Short Circuit and Venting

Mitigation of Li-ion battery system fires consists of reliable fault detection and proactive, fast discharge control. Both require modeling of failure modes due to high temperatures and currents between normal operation and thermal runaway. In this work, we present a control-oriented, reduced-order, multiphysics model that captures the electrochemical, thermal, gas generation, mechanical expansion, and venting behavior of NMC pouch cells undergoing an external short circuit (ESC) from different initial state-of-charge (SOC). The model is parameterized through experiments by fitting the solid-electrolyte interphase (SEI) decomposition rate, the cell’s thermal parameters, and the particle solid-phase diffusion parameters to capture the first venting timing, peak temperature, and diffusion-limited electrical behavior at high currents. Using a single parameter set, the multiphysics model can capture behavior during an ESC to predict whether a cell will generate gas and vent, predict the vent timing within 10 seconds of it occurring in the experiment, and maximum cell expansion pressure within 10 kPa for cells that did not vent. The model can also predict the SOC trajectory for cells with a high initial SOC within 6% SOC for the 15-minute discharge or until the cell vents.

VELOcities of CEpheids (VELOCE)

We present the first data release of VELOcities of CEpheids (VELOCE), dedicated to measuring the high-precision radial velocities (RVs) of Galactic classical Cepheids (henceforth, Cepheids). The first data release (VELOCE DR1) comprises 18 225 RV measurements of 258 bona fide classical Cepheids on both hemispheres collected mainly between 2010 and 2022, along with 1161 observations of 164 stars, most of which had previously been misclassified as Cepheids. The median per-observation RV uncertainty for Cepheids is 0.037 km s−1 and reaches 2 m s−1 for the brightest stars observed with Coralie. Non-variable standard stars were used to characterize RV zero-point stability and to provide a base for future cross-calibrations. We determined zero-point differences between VELOCE and 31 literature data sets using template fitting, which we also used to investigate linear period changes of 146 Cepheids. In total, 76 spectroscopic binary Cepheids and 14 candidate binary Cepheids were identified using VELOCE data alone, which are investigated in detail in a companion Paper (VELOCE II). VELOCE DR1 provides a number of new insights into the pulsational variability of Cepheids, most importantly: a) the most detailed description of the Hertzsprung progression based on RVs to date; b) the identification of double-peaked bumps in the pulsation curve; and c) clear evidence that virtually all Cepheids feature spectroscopic variability signals that lead to modulated RV variability at the level of tens to hundreds of m s−1 and that cannot be satisfactorily modeled using single-periodic Fourier series. We identified 36 stars exhibiting such modulated variability, of which 4 also exhibit orbital motion. Linear radius variations depend strongly on pulsation period and a steep increase in slope of the ΔR/p vs. log P-relation is found near 10 days. This effect, combined with significant RV amplitude differences at fixed period, challenges the existence of a tight relation between Baade-Wesselink projection factors and pulsation periods. We investigated the accuracy of RV time series measurements, υγ, and RV amplitudes published by Gaia’s third data release (Gaia DR3) and determined an offset of 0.65 ± 0.11 km s−1 relative to VELOCE. Whenever possible, we recommend adopting a single set of template correlation parameters for distinct classes of large-amplitude variable stars to avoid systematic offsets in υγ among stars belonging to the same class. The peak-to-peak amplitudes of Gaia RVs exhibit significant (16%) dispersion. Potential differences of RV amplitudes require further inspection, notably in the context of projection factor calibration.

A shear‐transformation‐zone model for time‐dependent behaviours of clay

AbstractA shear‐transformation‐zone (STZ) model is proposed for time‐dependent behaviours of clay, in which the viscoplastic deformation is described by the evolution of a temperature‐like state variable. The model is featured by rate‐dependent dilatancy and a unique critical state stress ratio. In its present form, it has nine parameters, most of which can be handily calibrated, and can predict the rate‐dependent undrained shear strength, primary, and secondary creep, as well as stress relaxation for normally consolidated clay under complex stress paths using a single set of parameters. The capabilities of the model have been verified by available experimental results on five different clays.

From a local descriptive to a generic predictive model of cereal aphid regulation by predators.

The temporal dynamics of insect populations in agroecosystems are influenced by numerous biotic and abiotic interactions, including trophic interactions in complex food webs. Predicting the regulation of herbivorous insect pests by arthropod predators and parasitoids would allow for rendering crop production less dependent on chemical pesticides. Curtsdotter etal. (2019) developed a food-web model simulating the influences of naturally occurring arthropod predators on aphid population dynamics in cereal crop fields. The use of an allometric hypothesis based on the relative body masses of the prey and various predator guilds reduced the number of estimated parameters to just five, albeit field-specific. Here, we extend this model and test its applicability and predictive capacity. We first parameterized the original model with a dataset with the dynamic arthropod community compositions in 54 fields in six regions in France. We then integrated three additional biological functions to the model: parasitism, aphid carrying capacity and suboptimal high temperatures that reduce aphid growth rates. We developed a multi-field calibration approach to estimate a single set of generic allometric parameters for a given group of fields, which would increase model generality needed for predictions. The original and revised models, when using field-specific parameterization, achieved quantitatively good fits to observed aphid population dynamics for 59% and 53% of the fields, respectively, with pseudo-R2 up to 0.99. But the multi-field calibration showed that increased model generality came at the cost of reduced model reliability (goodness-of-fit). Our study highlights the need to further improve our understanding of how body size and other traits affect trophic interactions in food webs. It also points up the need to acquire high-resolution data to use this type of modelling approach. We propose that a hypothesis-driven strategy of model improvement based on the integration of additional biological functions and additional functional traits beyond body size (e.g., predator space search or prey defences) into the food-web matrix can improve model reliability.

Multi-year three-dimensional simulation of seasonal variation in phytoplankton species composition in a large shallow lake

Lake Erie has been negatively impacted by multiple stressors, including nutrient enrichment and climate change, that have exacerbated eutrophication and harmful algal blooms. Management of these long-term water quality problems requires numerical models that can be run over years to decades. The three-dimensional hydrodynamics and biogeochemistry models applied to date, however, have not been tested for continuous runs longer than one year and have not been shown to accurately reproduce seasonal variation in phytoplankton species composition (e.g., the development of harmful algal blooms) over decadal timescales. We simulated the three-dimensional nutrient and phytoplankton concentrations in western Lake Erie continuously from 2002 to 2014. Using a single parameter set, we were able to reproduce both seasonal and inter-annual variation in phytoplankton species composition. The model qualitatively reproduced the observed seasonal succession (i.e., variation in phytoplankton species composition), including the spring diatom bloom and late summer cyanobacterial growth. This study demonstrates that three-dimensional models can be applied for multi-year simulations of nutrients and phytoplankton to inform large lake research and management.

Grey-Taguchi approach to optimize the tribological performance of hybrid composites

Modern manufacturing technology is evolving, necessitating the creation of materials with higher wear resistance. This research endeavors to forecast the dry sliding wear performance of AL6061-B4C-RM hybrid composites by employing Taguchi and grey relational analysis approaches. Present study applies the L16 Taguchi approach to optimize the process parameters specific wear, and coefficient of friction (COF) at four different levels. Using multiple responses for optimization for both responses, a single process parameter setting is obtained via grey relational analysis. In AL6061 composites, varying weight proportions of Boron Carbide particles (B4C) were added as reinforcements while keeping the red mud (RM) content constant at 2 %. To recognize the dominant factor, analysis of variance (ANOVA) was implemented, and the outcomes of the assessment showed speed plays a crucial role, followed by the weight percent of reinforcement and the load in determining the specific wear rate and COF. A research trial was completed to confirm the outcomes obtained from the combination of optimal parameters.

Numerical simulation of multi-material hybrid lines for offshore mooring

In the last decades, great attention is being given to the development and improvement of station-keeping systems of offshore structures, such as wind turbines, energy converters, and platforms. In deep- and ultra-deepwaters installations, the use of synthetic, light-weight materials in lieu of chains and wire ropes is almost mandatory because of the deadweight of the entire mooring system. Several materials are useful for such purpose, such as polyester (PET), aramid, high modulus polyethylene (HMPE), and polyamide. However, since each material has its own advantages and disadvantages, an alternative is to design multi-material lines, with inserts of different materials aligned in series in one complete single line. In this sense, numerical tools are of utmost importance to the simulation of the behavior of the ropes in service conditions, and the quality of the results depend on the accurate definition of material properties. Here, we propose a constitutive framework for the finite element simulation of multi-material mooring ropes and test it to five configurations of multifilament samples combining PET and HMPE. We show that the model is capable of simulating the mechanical behavior of all configurations with a single set of constitutive parameters.

Hydrophobic interactions described using hetero-segmented PC-SAFT: 1. Alcohol/water mixtures

The hydrophobic effect plays a central role in aqueous systems containing molecules with hydrophobic moieties. Despite the relevance of this effect for chemical processes or pharmaceutical applications, modeling remains challenging even using molecular-based models such as different versions of the Statistical Associating Fluid Theory. This work shows the feasibility of hetero-segmented PC-SAFT for this purpose. This model was used as a group contribution method to build molecules from different functional groups, namely from CH2, CH3, and CH2OH groups. Saturated-liquid densities and vapor pressures of n-alkanes and n-alcohols as well as binary mixtures of these molecules were accurately described by this approach. The description of aqueous mixtures containing n-alkanes and n-alcohols was improved compared to state-of-the-art modeling by explicitly accounting for intermolecular interactions resulting in the hydrophobic effect. The so-obtained framework describes the two liquid−liquid equilibrium phases in mixtures of n-alkanes or n-alcohols with water equally well using a single set of transferable parameters. The model was also validated for vapor–liquid equilibria, solid–liquid equilibria, infinite-dilution properties, as well as octanol/water partition coefficients and showed quantitative agreement with experimental data.

Characterizing urban spatial structure through built form typologies: A new framework using clustering ensembles

Prior research on urban form typologies has largely relied on qualitative classification methods, resulting in subjective and limited analyses. Recently, the emerging data-intensive studies often use a single clustering algorithm and parameter setting, raising concerns about the reliability of the findings. This paper introduces a novel clustering analytical framework for conducting typological studies on urban form that yield stable and reliable results. We employ clustering ensembles, which can combine multiple clustering algorithms to further provide a comprehensible output that facilitates interpretation and knowledge generation. By applying the new framework using 3D building data in Guangzhou, we identify eight typologies of urban built forms and reveal a consistent polycentric pattern across different clustering algorithms and parameter settings. The findings have implications for urban land use planning and regulations by integrating 3D representations of urban form.

A model for the temperature-dependent creep/recovery behavior of dry fiber fabric considering in-plane tension

In the manufacturing process of fiber-reinforced composite materials, the preforming of dry fiber fabrics is typically carried out under specific out-of-plane pressure, in-plane tension, and at designated temperatures. The fabric exhibits significant time and temperature-dependent permanent deformation as well as creep/recovery behavior. Gaining a better understanding of these phenomena and harnessing predictive capabilities will be instrumental in achieving more precise control over fiber volume fraction and material composition. In this paper, creep/recovery experiments and cyclic loading-unloading tests were conducted on CF3031 dry fiber preforms under various in-plane tensions and temperatures, measuring in-plane strains using Fiber Bragg Grating (FBG) sensors. Based on this data, a modified Burgers viscoelastic-plastic model has been proposed to describe the time-dependent creep/recovery behavior of dry fiber fabric preforming under the coupled effects of in-plane tension, out-of-plane pressure, and temperature variations. This model employs a single set of equations and parameters to represent the complete creep/recovery process of the material. The experimental results align well with the predicted outcomes across different compression scenarios.

Laser Test Spots: A Scoping Review.

Laser test spots are commonly suggested for the assessment of clinical response and adverse effects, but use by laser operators is not well described. To describe the use of laser test spots in the existing published literature regarding methodology (location, treatment parameters) and objective (clinical efficacy, safety, other). This scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and included indexed studies performing test spots in human subjects for dermatologic conditions with clinical reassessment at a subsequent visit. Among 5,261 identified publications, 103 studies with 959 test spots were selected for inclusion. Test spots conducted were mostly on lesional skin (89.3%) assessing both clinical response and adverse effects (76.9%). Most test spots used multiple laser parameters with a single wavelength (48.3%). Fluence was most frequently adjusted either alone (30.1%) or in combination with pulse duration or spot size. Other described test spots examined single set of laser parameters, multiple wavelengths with various parameters, or were left unspecified. Laser test spot methodology was diverse and performed for dual objectives of efficacy and safety. The authors have compiled clinical considerations to assist laser operators in deciding whether performing a test spot may be beneficial to their patient.

A creep/recovery model of dry fiber fabric during compaction considering temperature and in‐plane tension

AbstractIn the manufacturing of fiber‐reinforced composite materials, the preforming stage of dry fiber fabric entails compaction operations carried out under constant pressure, a specific preforming temperature, and in‐plane tension. Throughout this process, permanent deformation and creep/recovery behavior are observed in the fabric, correlating with factors such as time, sustained pressure, temperature, and in‐plane tension. Effective control of fiber volume fraction and material composition necessitates a thorough comprehension of these phenomena as well as the capacity to predict them with accuracy. To solve this, a viscoelastic‐plastic model was put forth, which provides an account of the viscous effects on the fabric during its stages of compression and recovery. The proposed model incorporates changed Burgers units to manage time‐dependent deformations and plastic units to address permanent deformations. Through the application of a single set of parameters, the model precisely and comprehensively characterizes through‐thickness creep/recovery behavior of preformed dry fiber fabrics under varying conditions, including creep stress, temperature, and in‐plane tension. To enhance the practical utility of the model, an application has been developed and validated against experimental data conducted under diverse test conditions. The results demonstrate consistency between experimental curves and model‐predicted curves across various compression scenarios.Highlights A viscoelastic‐plastic model was proposed with a single parameter set. Stress, temperature, and in‐plane tension all exacerbated creep/recovery. Model captures temp. and time‐related creep/recovery with in‐plane tension. The model has been transformed into a practical application tool.

A new test of gravity – I. Introduction to the method

ABSTRACT We introduce a new scheme based on the marked correlation function to probe gravity using the large-scale structure of the Universe. We illustrate our approach by applying it to simulations of the metric-variation f(R) modified gravity theory and general relativity (GR). The modifications to the equations in f(R) gravity lead to changes in the environment of large-scale structures that could, in principle, be used to distinguish this model from GR. Applying the Monte Carlo Markov Chain algorithm, we use the observed number density and two-point clustering to fix the halo occupation distribution (HOD) model parameters and build mock galaxy catalogues from both simulations. To generate a mark for galaxies when computing the marked correlation function we estimate the local density using a Voronoi tessellation. Our approach allows us to isolate the contribution to the uncertainty in the predicted marked correlation function that arises from the range of viable HOD model parameters, in addition to the sample variance error for a single set of HOD parameters. This is critical for assessing the discriminatory power of the method. In a companion paper, we apply our new scheme to a current large-scale structure survey.

DC‐bias and temperature included CSWPL model for RF power transistors

AbstractA novel frequency domain behavioral modeling method for gallium‐nitride (GaN) devices is presented in this article. By utilizing a multi‐dimensional polynomial function, the proposed technique interpolates DC‐bias voltage and temperature based on the Canonical section‐wise piecewise linear (CSWPL) model framework. A detailed description of the model's theory is provided. With data from 10‐W GaN devices, the model was implemented in commercial software and validated through both DC and radio frequency (RF) tests. A robust predictive ability is demonstrated by the obtained results, thus proving the accuracy of the developed modeling method. This model is superior to the standard CSWPL model in that it is capable of predicting transistor behavior at different bias voltages and temperatures using a single set of parameters, thereby greatly reducing the complexity of the model and the time required for extraction.

Temperature and strain rate effects on ultra‐high‐molecular‐weight‐polyethylene compression: An experimental and modeling approach

AbstractThis study aims to predict the compression behavior of ultra‐high molecular weight polyethylene (UHMWPE) at various temperatures (25, 40, and 55°C) and strain rates (~10−4/s and ~10−2/s) using a single set of three‐network (TN) viscoplastic model parameters. The TN model is made up of three parallel networks: networks A and B use nonlinear springs and dashpots to control the responses of the crystalline phase and confined amorphous phases, respectively, while network C captures the macromolecular amorphous networks using a single nonlinear spring. A single set of TN model parameters captures the yield and post‐yield hardening responses of the stress–strain curves at experimental temperatures and strain rates. When these TN model calibrated parameters are used as material property in a commercial finite element tool to simulate UHMWPE compression, the predicted and simulated results match well, showing the model's fidelity. Additionally, the model predicts experiments conducted at 40 and 70°C with loading rates of ~10−3/s and ~10−2/s, respectively. The study also correlates the deformations of UHMWPE's crystalline structure and macromolecular amorphous networks with its global stress versus strain response by extracting stresses from individual networks of the TN model at different strain rates and temperatures.Highlights UHMWPE's mechanical behavior predicted by a single set of TN model parameters TN model predicts mechanical response at various strain rates and temperatures. TN model explains microstructural crystalline and amorphous phase deformations.

Analytical model for the drain and gate currents in silicon nanowire and nanosheet MOS transistors valid between 300 and 500 K

AbstractThis work presents an analytical model for the drain and gate currents of silicon nanowire and nanosheet MOS transistors valid in all operating regions in the temperature range from 300 to 500 K. Analytical models for the tunneling components as well as for the reversely biased drain‐to‐channel PN junction are presented. Also, the models accounting for the necessary modifications in the silicon physical quantities for high‐temperature operation, such as the maximum carrier mobility, the bandgap, and the intrinsic carrier concentration, are presented. The proposed model uses a single set of parameters, extracted at room temperature, to describe the high‐temperature operation of silicon nanowire MOSFETs. The model is validated with comparisons between modeled and experimental results for devices with different fin widths and operating temperatures, with good agreement.

Fractional PID with Genetic Algorithm Approach for Industrial Tank Level Control Process

Process control is used in various types of industries to boost production while using current resources, product quality, and safety, with level control being one of the most important schemes. It is suggested that study be conducted on nonlinear continuous processes, particularly in process control applications such as conical tank level control. Initially, each process receives one set of Proportional-Integral (PI) controller settings and a First Order Plus Dead-Time (FOPDT) model using the Ziegler Nicholas (ZN) step response tuning method. The numerous FOPDT models and parameter sets are also obtained. Responses obtained with a single set of parameters and responses obtained with many sets of parameters are compared. By creating a Fractional PID Controller (FPID) with Genetic Algorithm (GA), a simulation-based tuning strategy is proposed. The step responses are acquired through experimental research using fine-tuned settings. At most operational points, the Fractional PID Controller with Genetic Algorithm-based tuning approach outperforms, with lowest oscillation, extremely tiny overshoot, minimum average ISE, and minimum average IAE. The proposed FPID with GA method provides better setpoint and regulatory tracking performance compared to ZN-PID.